I. Read in libraries and data

II. Tabular Data Cleaning and Calculation

III. Import Geo Data and Data Merge

Just so you get a general idea of how Shanghai is. We are large. A city more than 1/5 area of Belgium. The outlying districts of course are populated, and with economic growth and urbanization, more and more people come to work in Shanghai, yet they might not have enough money to buy, or even rent, apartments in more central districts, so the outlying districts would see more population growth. But at this moment, most people still live in these orange and red areas. But don't get the idea wrong. They look compact on the map, but commuting from, say, Putuo District (dark red) to Pudong (orange, the largest district) would still take more than an hour, by our very efficient metro system.

So, although "浦东新区 / Pudong" has the highest number of listings (because it's a really big district - biggest area)(see "district1"), but it's still in the central districts where the listings are most dense.

You'll see in the map below, that the distribution is quite similar to the one of population density. The listings are more concentrated in the central districts. (Cause these are where the fun stuff are. ;) )

Now the geographical data "districts" is finally done!

IV. Spatial Join

V. Some Questions and Analysis

1. What's the average price for each room type in each district?

Interesting... It's the outlying districts that have the highest average prices. And Mostly it's because of the "Entire home/apt". I guess they have really luxurious apartments in those areas.

And you'll see in the map below that it looks quite the opposite to the one visualizing density of listings.

2. What's the total and average monthly rental income in each neighbourhood?

It looks actually quite similar to the price map. I guess the occupancy rates of those high-price apartments/rooms are not bad, so higher-price neighbourhoods also have higher monthly rental income.

3. Is there be any relation between occupancy rate and price?

4. What is the share of listings of each room type per neighbourhood?

5. How many airbnb hosts are there in Shanghai? And how many are professional hosts (listing > 1)?

6. Which are the top 20 hosts who make most money?

Since I can't read much from this table, I try to "normalize" the data a little bit.

7. Which district has the highest share of listings owned by professional hosts?

Why am I making this map? To show that this district is where I've been living for most of my life. This is also where many working-class people live, for decades, esp. from the 80s to 2000s. This is the district where listings are least owned by professional hosts. (And the prof hosts might not live here, they just own properties here.) ~ Hence hooray we modest proletarians. ~

8. What is the difference between the total monthly rental income per host for amateurs (1 listing only) and professionals (> 1 listing)?

A big difference! Six times!

9. How does the overall occupancy rate look like?

The total number of listings (after the data is cleaned) is 16910. So looks like around 2/3 listings hit max occupancy rate.

10. Where are these listings that hit maximum occupancy rate, then?

For those listings whose occupancy rates are lower than 70%, I don't have much insight. I visualize them just to have a sense of completion.

Voilà, c'est tout. I hope this is both good enough and not too much. Thank you for your time!